Briefings in Bioinformatics

Article	Citations
Dynamic changes of synergy relationship between lncRNA and immune checkpoint in cancer progression	933
Phage quest: a beginner’s guide to explore viral diversity in the prokaryotic world	568
Deep learning reveals determinants of transcriptional infidelity at nucleotide resolution in the allopolyploid line by goldfish and common carp hybrids	556
Computational model for ncRNA research	473
COWID: an efficient cloud-based genomics workflow for scalable identification of SARS-COV-2	349
QOT: Quantized Optimal Transport for sample-level distance matrix in single-cell omics	296
CpGFuse: a holistic approach for accurate identification of methylation states of DNA CpG sites	243
ETLD: an encoder-transformation layer-decoder architecture for protein contact and mutation effects prediction	241
Ensemble classification based feature selection: a case of identification on plant pentatricopeptide repeat proteins	231
Correction to: Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology	218
SCSMD: Single Cell Consistent Clustering based on Spectral Matrix Decomposition	215
Addressing scalability and managing sparsity and dropout events in single-cell representation identification with ZIGACL	203
Machine learning–augmented m6A-Seq analysis without a reference genome	186
Attribute-guided prototype network for few-shot molecular property prediction	182
Hi-C3: a statistical inference-based model for reconstructing higher-order cell–cell communication networks	159
Systematic evaluation of de novo mutation calling tools using whole genome sequencing data	152
QTFPred: robust high-performance quantum machine learning modeling that predicts main and cooperative transcription factor bindings with base resolution	137
Stoichiometry-preserving and stochasticity-aware identification of m6A from direct RNA sequencing	131
FGeneBERT: function-driven pre-trained gene language model for metagenomics	129
Assessing protein model quality based on deep graph coupled networks using protein language model	127
Learning discriminative and structural samples for rare cell types with deep generative model	124
Predicting microbe–drug associations with structure-enhanced contrastive learning and self-paced negative sampling strategy	120
Towards comprehensive benchmarking of medical vision language models	115
A novel prognostic framework for HBV-infected hepatocellular carcinoma: insights from ferroptosis and iron metabolism proteomics	112
Improving the performance of single-cell RNA-seq data mining based on relative expression orderings	110

Predicting protein–carbohydrate binding sites: a deep learning approach integrating protein language model embeddings and structural features	110
MicroHDF: predicting host phenotypes with metagenomic data using a deep forest-based framework	109
Ensemble learning based on matrix completion improves microbe-disease association prediction	108
Genome assembly and gene identification of biosurfactant-producing bacteria for environmental bioremediation	105
Benchmarking of computational methods for m6A profiling with Nanopore direct RNA sequencing	105
dHICA: a deep transformer-based model enables accurate histone imputation from chromatin accessibility	103
Melanoma 2.0. Skin cancer as a paradigm for emerging diagnostic technologies, computational modelling and artificial intelligence	101
Evaluating large language models for annotating proteins	100
Protein–DNA binding sites prediction based on pre-trained protein language model and contrastive learning	98
AICellType: a large language model-based platform for accurate cell type annotation	95
Computational refinement and multivalent engineering of complementarity-determining region-grafted nanobodies on a humanized scaffold for retaining antiviral efficacy	95
Building multiscale models with PhysiBoSS, an agent-based modeling tool	95
A chronotherapeutics-applicable multi-target therapeutics based on AI: Example of therapeutic hypothermia	92
Self-supervised learning with chemistry-aware fragmentation for effective molecular property prediction	91
Multi-marker testing based on accelerated failure time models under possible left truncation and competing risks	91
A social theory-enhanced graph representation learning framework for multitask prediction of drug–drug interactions	90
Cox-Sage: enhancing Cox proportional hazards model with interpretable graph neural networks for cancer prognosis	89
Graph-RPI: predicting RNA–protein interactions via graph autoencoder and self-supervised learning strategies	88
Machine learning modeling of RNA structures: methods, challenges and future perspectives	88
Large-scale predicting protein functions through heterogeneous feature fusion	87
IGCNSDA: unraveling disease-associated snoRNAs with an interpretable graph convolutional network	86
scAnno: a deconvolution strategy-based automatic cell type annotation tool for single-cell RNA-sequencing data sets	85
Directed evolution of antimicrobial peptides using multi-objective zeroth-order optimization	85
Inferring disease-associated circRNAs by multi-source aggregation based on heterogeneous graph neural network	84
GeNePi: a graphics processing unit enhanced next-generation bioinformatics pipeline for whole-genome sequencing analysis	83
Clustering scRNA-seq data with the cross-view collaborative information fusion strategy	83
Blood-based transcriptomic signature panel identification for cancer diagnosis: benchmarking of feature extraction methods	80
mbDecoda: a debiased approach to compositional data analysis for microbiome surveys	78
CLT-seq as a universal homopolymer-sequencing concept reveals poly(A)-tail-tuned ncRNA regulation	78
A comprehensive benchmark of tools for efficient genomic interval querying	78
PRIEST: predicting viral mutations with immune escape capability of SARS-CoV-2 using temporal evolutionary information	77
BayesKAT: bayesian optimal kernel-based test for genetic association studies reveals joint genetic effects in complex diseases	77
DeepCheck: multitask learning aids in assessing microbial genome quality	77
A robust statistical approach for finding informative spatially associated pathways	75
DriverOmicsNet: an integrated graph convolutional network for multi-omics exploration of cancer driver genes	74
Multi-modal domain adaptation for revealing spatial functional landscape from spatially resolved transcriptomics	74
Improving drug response prediction via integrating gene relationships with deep learning	73
Making PBPK models more reproducible in practice	73
ULDNA: integrating unsupervised multi-source language models with LSTM-attention network for high-accuracy protein–DNA binding site prediction	72
A multichannel graph neural network based on multisimilarity modality hypergraph contrastive learning for predicting unknown types of cancer biomarkers	72
PLMFit: benchmarking transfer learning with protein language models for protein engineering	72
Deep learning in integrating spatial transcriptomics with other modalities	71
Subtype-DCC: decoupled contrastive clustering method for cancer subtype identification based on multi-omics data	71
Clover: tree structure-based efficient DNA clustering for DNA-based data storage	71
STEAM: Spatial Transcriptomics Evaluation Algorithm and Metric for clustering performance	71
HighFold: accurately predicting structures of cyclic peptides and complexes with head-to-tail and disulfide bridge constraints	70
GAABind: a geometry-aware attention-based network for accurate protein–ligand binding pose and binding affinity prediction	69
Clustered tree regression to learn protein energy change with mutated amino acid	68
Analysis of super-enhancer using machine learning and its application to medical biology	68
Multiple errors correction for position-limited DNA sequences with GC balance and no homopolymer for DNA-based data storage	68

Identification of vital chemical information via visualization of graph neural networks	67
cfMethylPre: deep transfer learning enhances cancer detection based on circulating cell-free DNA methylation profiling	65
scGAD: a new task and end-to-end framework for generalized cell type annotation and discovery	65
AptaDiff: de novo design and optimization of aptamers based on diffusion models	64
Novel multi-omics deconfounding variational autoencoders can obtain meaningful disease subtyping	62
Beyond metaphor: quantitative reconstruction of Waddington landscape and exploration of cellular behavior	62
Detecting tipping points of complex diseases by network information entropy	61
Integrating AlphaFold and deep learning for atomistic interpretation of cryo-EM maps	61
Protein phosphorylation database and prediction tools	61
Nonlinear kernel-based high-dimensional inference for set-based genetic association studies	61
PMiSLocMF: predicting miRNA subcellular localizations by incorporating multi-source features of miRNAs	60
Deciphering gene contributions and etiologies of somatic mutational signatures of cancer	60
From intuition to AI: evolution of small molecule representations in drug discovery	60
TransIntegrator: capture nearly full protein-coding transcript variants via integrating Illumina and PacBio transcriptomes	59
Systematic investigation of the homology sequences around the human fusion gene breakpoints in pan-cancer – bioinformatics study for a potential link to MMEJ	59
scAMAC: self-supervised clustering of scRNA-seq data based on adaptive multi-scale autoencoder	59
Correction to: sciCNV: high-throughput paired profiling of transcriptomes and DNA copy number variations at single-cell resolution	59
Complexity of enhancer networks predicts cell identity and disease genes revealed by single-cell multi-omics analysis	59
Inferring kinase–phosphosite regulation from phosphoproteome-enriched cancer multi-omics datasets	59
TCM-navigator, a deep learning-based workflow for generation and evaluation of traditional Chinese medicine-like compounds for drug development	59
Correction to: Computational toxicology in drug discovery: applications of artificial intelligence in ADMET and toxicity prediction	58
Integrated multimodal hierarchical fusion and meta-learning for enhanced molecular property prediction	58
SAMURAI: shallow analysis of copy number alterations using a reproducible and integrated bioinformatics pipeline	58
A novel heterophilic graph diffusion convolutional network for identifying cancer driver genes	57
scEWE: high-order element-wise weighted ensemble clustering for heterogeneity analysis of single-cell RNA-sequencing data	57
AI-assisted patient matching for personalized cancer medicine	57
A comprehensive benchmarking of differential splicing tools for RNA-seq analysis at the event level	56
A novel approach to study multi-domain motions in JAK1’s activation mechanism based on energy landscape	56
BETA: a comprehensive benchmark for computational drug–target prediction	56
ncRNAInter: a novel strategy based on graph neural network to discover interactions between lncRNA and miRNA	56
Denoising adaptive deep clustering with self-attention mechanism on single-cell sequencing data	56
Multilevel superposition for deciphering the conformational variability of protein ensembles	55
Data-driven selection of analysis decisions in single-cell RNA-seq trajectory inference	55
SPNE: sample-perturbed network entropy for revealing critical states of complex biological systems	55
slORFfinder: a tool to detect open reading frames resulting from trans-splicing of spliced leader sequences	55
Interpretable high-order knowledge graph neural network for predicting synthetic lethality in human cancers	55
Improving multi-population genomic prediction accuracy using multi-trait GBLUP models which incorporate global or local genetic correlation information	54
Clinical and data-driven optimization of Genomiser for rare disease patients: experience from the Hong Kong Genome Project	54
Nativeness-constrained diffusion framework for nanobody design	54
Therapeutic peptides identification via kernel risk sensitive loss-based k-nearest neighbor model and multi-Laplacian regularization	54
Advancing microbial diagnostics: a universal phylogeny guided computational algorithm to find unique sequences for precise microorganism detection	54
RBP-TSTL is a two-stage transfer learning framework for genome-scale prediction of RNA-binding proteins	54
Knowledge-guided multi-level network modeling with experimental characterization identifies PRKCA as a novel biomarker and tumor suppressor triggering ferroptosis in prostate cancer	54
SPANN: annotating single-cell resolution spatial transcriptome data with scRNA-seq data	54
A comprehensive computational benchmark for evaluating deep learning-based protein function prediction approaches	53
CHAI: consensus clustering through similarity matrix integration for cell-type identification	53
Forecasting dominance of SARS-CoV-2 lineages by anomaly detection using deep AutoEncoders	52
Efficient prediction of peptide self-assembly through sequential and graphical encoding	52
A risk assessment framework for multidrug-resistant Staphylococcus aureus using machine learning and mass spectrometry technology	52
Robust discovery of gene regulatory networks from single-cell gene expression data by Causal Inference Using Composition of Transactions	52
Deep learning in structural bioinformatics: current applications and future perspectives	52
FactVAE: a factorized variational autoencoder for single-cell multi-omics data integration analysis	51
OmniDoublet: a method for doublet detection in multimodal single-cell sequencing data	51
BioWorkflow: Retrieving comprehensive bioinformatics workflows from publications	51
A novel computational model ITHCS for enhanced prognostic risk stratification in ESCC by correcting for intratumor heterogeneity	51
scAED: a framework for mapping the enhancer state at single-cell resolution	51
The improved de Bruijn graph for multitask learning: predicting functions, subcellular localization, and interactions of noncoding RNAs	51
Estimation of non-equilibrium transition rate from gene expression data	51
Cross-modality representation and multi-sample integration of spatially resolved omics data	50
Drug repositioning based on weighted local information augmented graph neural network	50
Robustness and resilience of computational deconvolution methods for bulk RNA sequencing data	50
Reconstructing 3D transcriptional organization from spatial transcriptomics reveals consistent oncogenic translocations and developmental dynamics	50
BatchDTA: implicit batch alignment enhances deep learning-based drug–target affinity estimation	50
ConSIG: consistent discovery of molecular signature from OMIC data	50
Toward high-efficiency, low-resource, and explainable neuropeptide prediction with MSKDNP	50
AI-guided discovery and optimization of antimicrobial peptides through species-aware language model	50
Predicting molecular properties based on the interpretable graph neural network with multistep focus mechanism	49
Component puzzle protein–protein interaction prediction	49
PSnoD: identifying potential snoRNA-disease associations based on bounded nuclear norm regularization	49
Multi-omics regulatory network inference in the presence of missing data	49
scDeepInsight: a supervised cell-type identification method for scRNA-seq data with deep learning	48
SGCLDGA: unveiling drug–gene associations through simple graph contrastive learning	48
TaxaGO: a novel, phylogenetically informed gene ontology enrichment analysis tool	47
Estimating population structure using epigenome-wide methylation data	47
Quantifying transcript complexity via the condition number of gene-specific random matrix	47
HLAIImaster: a deep learning method with adaptive domain knowledge predicts HLA II neoepitope immunogenic responses	47
Development and validation of an explainable machine learning model for predicting multidimensional frailty in hospitalized patients with cirrhosis	47
MSF-CPMP: a novel multi-source feature fusion model for prediction of cyclic peptide membrane permeability	46
Comprehensive machine learning boosts structure-based virtual screening for PARP1 inhibitors	46
Contrastive learning-based computational histopathology predict differential expression of cancer driver genes	46

Metatranscriptomic analysis uncovers microbial and immune signatures underlying COVID-19 severity	46
AnnoAgent: a language agent for single-cell automatic annotation	46
ReCIDE: robust estimation of cell type proportions by integrating single-reference-based deconvolutions	46
Phylogenetic inference of inter-population transmission rates for infectious diseases	45
IEPAPI: a method for immune epitope prediction by incorporating antigen presentation and immunogenicity	45
PGVDA: a pathway-aggregated genetic dosage framework for interpretable disease classification using machine learning	45
A review of methods for predicting DNA N6-methyladenine sites	45
Enhancing protein structure prediction: evaluating the role of amino acid physicochemical features in homology search	45
Structure-enhanced deep learning accelerates aptamer selection for small molecule families like steroids	44
A transformer-based deep learning survival prediction model and an explainable XGBoost anti-PD-1/PD-L1 outcome prediction model based on the cGAS-STING-centered pathways in hepatocellular carcinoma	43
Paradigms, innovations, and biological applications of RNA velocity: a comprehensive review	43
dSCOPE: a software to detect sequences critical for liquid–liquid phase separation	43
ceQTL: a co-expression QTL model to detect a variant that affects transcription factor binding and its target regulation	43
PredLLPS_PSSM: a novel predictor for liquid–liquid protein separation identification based on evolutionary information and a deep neural network	43
Comparative epigenome analysis using Infinium DNA methylation BeadChips	43
Inferring single-cell resolution spatial gene expression via fusing spot-based spatial transcriptomics, location, and histology using GCN	43
Beyond static structures: protein dynamic conformations modeling in the post-AlphaFold era	43
iEnhance: a multi-scale spatial projection encoding network for enhancing chromatin interaction data resolution	42
Revealing the antimicrobial potential of traditional Chinese medicine through text mining and molecular computation	42
Towards accurate artificial intelligence models for strain-level phage–host prediction	42
Optimized phenotyping of complex morphological traits: enhancing discovery of common and rare genetic variants	42
Seq2Topt: a sequence-based deep learning predictor of enzyme optimal temperature	42
MulNet: a scalable framework for reconstructing intra- and intercellular signaling networks from bulk and single-cell RNA-seq data	42
Concepts and methods for transcriptome-wide prediction of chemical messenger RNA modifications with machine learning	42
EDS-Kcr: deep supervision based on large language model for identifying protein lysine crotonylation sites across multiple species	41
PepTCR-Net: prediction of multi-class antigen peptides by T-cell receptor sequences with deep learning	41
Towards Comprehensive Benchmarking of Medical Vision Language Models	41
Mapping cancer heterogeneity: a consensus network approach to subtypes and pathways	41
Correction to: Diagnostic Prediction of portal vein thrombosis in chronic cirrhosis patients using data-driven precision medicine model	41
MegSite: an accurate nucleic acid-binding residue prediction method based on multimodal protein language model	41
Identification of molecular subtypes of dementia by using blood-proteins interaction-aware graph propagational network	41
Learning genotype–phenotype associations from gaps in multi-species sequence alignments	41
MetaGeno: a chromosome-wise multi-task genomic framework for ischaemic stroke risk prediction	41
A kinetic model for solving a combination optimization problem in ab-initio Cryo-EM 3D reconstruction	41
Predictive modelling of acute Promyelocytic leukaemia resistance to retinoic acid therapy	41
Predictive multispecies constraint-based metabolic modeling: case studies and best practices	40
CosGeneGate selects multi-functional and credible biomarkers for single-cell analysis	40
D3EGFR: a webserver for deep learning-guided drug sensitivity prediction and drug response information retrieval for EGFR mutation-driven lung cancer	40
Integrative analysis of multi-omics and imaging data with incorporation of biological information via structural Bayesian factor analysis	40
Spatially contrastive variational autoencoder for deciphering tissue heterogeneity from spatially resolved transcriptomics	40
Could statistical potential models achieve comparable or better performance than deep learning models?	40
ComABAN: refining molecular representation with the graph attention mechanism to accelerate drug discovery	40
ST-GCP: a graph convolutional network model with contrastive consistency and permutation for spatial transcriptomics	40
Incremental modelling and analysis of biological systems with fuzzy hybrid Petri nets	40
The landscape of the methodology in drug repurposing using human genomic data: a systematic review	40
Cross-RNA transferable sequence representation learning for lncRNA m6A site detection via novel deep domain separation networks	40
Adjustment of scRNA-seq data to improve cell-type decomposition of spatial transcriptomics	39
GiGs: graph-based integrated Gaussian kernel similarity for virus–drug association prediction	39
Combining evolution and protein language models for an interpretable cancer driver mutation prediction with D2Deep	39
Whole-genome bisulfite sequencing data analysis learning module on Google Cloud Platform	39
Predicting miRNA-disease associations based on graph attention networks and dual Laplacian regularized least squares	39
Few-shot drug synergy prediction via rapid cross-tier adaptation meta-optimization	39
Disrupting explicit encoding paradigms: property-interactive transformers decode T-cell receptor specificity beyond dataset biases	39
A comprehensive benchmark study of methods for identifying significantly perturbed subnetworks in cancer	39
DRdriver: identifying drug resistance driver genes using individual-specific gene regulatory network	39
Interpretable artificial intelligence model for accurate identification of medical conditions using immune repertoire	38
Deciphering hierarchical regulatory network of cell fate via an epigenetics-informed heterogeneous graph transformer on single-cell multi-omics data	38
An automatic immunofluorescence pattern classification framework for HEp-2 image based on supervised learning	38
Benchmarking genome assembly methods on metagenomic sequencing data	38
Matrix reconstruction with reliable neighbors for predicting potential MiRNA–disease associations	38
Data-driven patient stratification of UK Biobank cohort suggests five endotypes of multimorbidity	38
Bioinformatics toolbox for exploring target mutation-induced drug resistance	38
Exploring the kinase-inhibitor fragment interaction space facilitates the discovery of kinase inhibitor overcoming resistance by mutations	38
MGEGFP: a multi-view graph embedding method for gene function prediction based on adaptive estimation with GCN	38
BloodNet: An attention-based deep network for accurate, efficient, and costless bloodstain time since deposition inference	38
Microbe-bridged disease-metabolite associations identification by heterogeneous graph fusion	37
Current computational tools for protein lysine acylation site prediction	37
PPRS-ID: Indonesian-adjusted partitioned PRS for type 2 diabetes using obesity PRS integration and west Javanese population LD mapping	37
graphB3—an interpretable graph learning approach for predicting blood–brain barrier permeability	37
Advancing edge-based clustering and graph embedding for biological network analysis: a case study in RASopathies	37
Uncovering allosteric communication in cancer-related histone mutations	37
SAM-DTA: a sequence-agnostic model for drug–target binding affinity prediction	37
Evaluation of single-cell RNAseq labelling algorithms using cancer datasets	37
Causal Temporal Diffusion Networks for Drug Repurposing in Epilepsy	37
Predicting differentially methylated cytosines in TET and DNMT3 knockout mutants via a large language model	36
Single-cell mosaic integration and cell state transfer with auto-scaling self-attention mechanism	36
Machine learning-assisted substrate binding pocket engineering based on structural information	36
CELLetter: leveraging large language model and dual-stream network to identify context-specific ligand–receptor interactions for cell–cell communication analysis	36
Master of Metals2: a graph neural network based architecture for the prediction of zinc binding sites in protein structures	36
Learning single-cell chromatin accessibility profiles using meta-analytic marker genes	36
CACIMAR: cross-species analysis of cell identities, markers, regulations, and interactions using single-cell RNA sequencing data	36
scEGG: an exogenous gene-guided clustering method for single-cell transcriptomic data	36
Advancing single-cell RNA-seq data analysis through the fusion of multi-layer perceptron and graph neural network	36
HHOMR: a hybrid high-order moment residual model for miRNA-disease association prediction	36
MUSCLE: multi-view and multi-scale attentional feature fusion for microRNA–disease associations prediction	36
Toward next-generation machine learning and deep learning for spatial omics	36
Circling in on plasmids: benchmarking plasmid detection and reconstruction tools for short-read data from diverse species	35
A review of biomedical datasets relating to drug discovery: a knowledge graph perspective	35
Current approaches and outstanding challenges of functional annotation of metabolites: a comprehensive review	35
Improved prediction of DNA and RNA binding proteins with deep learning models	35
siRNADiscovery: a graph neural network for siRNA efficacy prediction via deep RNA sequence analysis	35
GSTRPCA: irregular tensor singular value decomposition for single-cell multi-omics data clustering	35
Identify potential drug candidates within a high-quality compound search space	35
Multi-level multi-view network based on structural contrastive learning for scRNA-seq data clustering	35
A deep learning method for predicting metabolite–disease associations via graph neural network	34